Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies.

BACKGROUND: A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security. OBJECTIVE: This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS. METHODS: A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS. RESULTS: A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies. CONCLUSIONS: This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks.

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications.

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.

Generalizable Layered Blockchain Architecture for Health Care Applications: Development, Case Studies, and Evaluation.

BACKGROUND: Data coordination across multiple health care facilities has become increasingly important for many emerging health care applications. Distrust has been recognized as a key barrier to the success of such applications. Leveraging blockchain technology could provide potential solutions tobuild trust between data providers and receivers by taking advantage of blockchain properties such as security, immutability, anonymity, decentralization, and smart contracts. Many health technologies have empirically proven that blockchain designs fit well with the needs of health care applications with certain degrees of success. However, there is a lack of robust architecture to provide a practical framework for developers to implement applications and test the performance of stability, efficiency, and scalability using standard blockchain designs. A generalized blockchain model is needed for the health care community to adopt blockchain technology and develop applications in a timely fashion. OBJECTIVE: This study aimed at building a generalized blockchain architecture that provides data coordination functions, including data requests, permission granting, data exchange, and usage tracking, for a wide spectrum of health care application developments. METHODS: An augmented, 3-layered blockchain architecture was built on a private blockchain network. The 3 layers, from bottom to top, are as follows: (1) incorporation of fundamental blockchain settings and smart contract design for data collection; (2) interactions between the blockchain and health care application development environment using Node.js and web3.js; and (3) a flexible development platform that supports web technologies such as HTML, https, and various programing languages. Two example applications, health information exchange (HIE) and clinical trial recruitment, were developed in our design to demonstrate the feasibility of the layered architecture. Case studies were conducted to test the performance in terms of stability, efficiency, and scalability of the blockchain system. RESULTS: A total of 331,142 simulated HIE requests from accounts of 40,000 patients were successfully validated through this layered blockchain architecture with an average exchange time of 11.271 (SD 2.208) seconds. We also simulated a clinical trial recruitment scenario with the same set of patients and various recruitment criteria to match potential subjects using the same architecture. Potential subjects successfully received the clinical trial recruitment information and granted permission to the trial sponsors to access their health records with an average time of 3.07 seconds. CONCLUSIONS: This study proposes a generalized layered blockchain architecture that offers health technology community blockchain features for application development without requiring developers to have extensive experience with blockchain technology. The case studies tested the performance of our design and empirically proved the feasibility of the architecture in 2 relevant health application domains.

Development of A Blockchain Framework for Virtual Clinical Trials.

Clinical trials are essential for discovering new treatments, but there are multiple challenges to patient recruitment, patient engagement, and cost containment. Virtual clinical trials (VCT) are an innovative approach that provides potential solutions by conducting home-based, rather than site-based, clinical trials. Virtual clinical trials are still the exception rather than general practice due to technical barriers. "Blockchain," a distributed ledger technology, is a perfect match for virtual clinical trials. Its peer-to-peer design, security settings, and data transparency meet the needs of many healthcare applications. The programmable "Smart Contract" feature makes blockchain more suitable and feasible for VCT by solving computational issues. Our previous work has shown the power of applying blockchain to clinical trial recruitment. This work develops a comprehensive blockchain framework, with simulations and case studies, including patient recruitment, patient engagement, and persistent monitoring modules.

A Patient-Centric Health Information Exchange Framework Using Blockchain Technology.

Health Information Exchange (HIE) exhibits remarkable benefits for patient care such as improving healthcare quality and expediting coordinated care. The Office of the National Coordinator (ONC) for Health Information Technology is seeking patient-centric HIE designs that shift data ownership from providers to patients. There are multiple barriers to patient-centric HIE in the current system, such as security and privacy concerns, data inconsistency, timely access to the right records across multiple healthcare facilities. After investigating the current workflow of HIE, this paper provides a feasible solution to these challenges by utilizing the unique features of blockchain, a distributed ledger technology which is considered "unhackable". Utilizing the smart contract feature, which is a programmable self-executing protocol running on a blockchain, we developed a blockchain model to protect data security and patients' privacy, ensure data provenance, and provide patients full control of their health records. By personalizing data segmentation and an "allowed list" for clinicians to access their data, this design achieves patient-centric HIE. We conducted a large-scale simulation of this patient-centric HIE process and quantitatively evaluated the model's feasibility, stability, security, and robustness.

Association between neurodegenerative diseases and pneumonia: a retrospective population-based study.

PURPOSE: The association between pneumonia and neurodegenerative diseases (NDs) has never been reported in detail. We address this relationship with reference to the general population. METHODS: Using Taiwan's National Health Insurance Research Database to identify a pneumonia cohort (including the typical and atypical), we established an ND cohort of 19,062 patients and a non-ND cohort of 76,227 people. In both cohorts, the risk of pneumonia was measured using multivariable Cox proportional hazards models. RESULTS: The adjusted hazard ratio (aHR) (95% confidence interval [CI]) for the pneumonia cohort was 2.10 (1.96-2.24), regardless of age, sex, comorbidities or drug use in the ND cohort. The aHR (95% CI) for adults aged 20-49 years was 2.08 (1.58-2.75), men 2.20 (2.01-2.40). However, older subjects were at greatest risk of pneumonia, (3.41 [2.99-3.88]) if the 20-49 years age group is used as the reference. For the ND and non-ND cohorts, those with comorbidities (with the exception of hyperlipidemia) had higher risk; aHR (95% CI) 2.35 (2.30-2.52). The aHR (95% CI) for those without comorbidities is 3.28 (2.52-4.26). No significant difference was observed in incidence of pneumonia between those who were and were not using statin medications; the aHR (95% CI) was 1.03 (0.93-1.14). CONCLUSION: The ND cohort had a higher risk of pneumonia, regardless of age, sex, comorbidities or statin use. The risk of pneumonia was higher in elderly and male patients in the ND cohort.